mlsanityData to 7 Oct 2026

Summarization, Simplification, and Generation: The Case of Patents

arXiv 2104.14860 ↗First posted 30 Apr 2021cs.CLcited by 6

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Abstract (arXiv)

We survey Natural Language Processing (NLP) approaches to summarizing, simplifying, and generating patents' text. While solving these tasks has important practical applications - given patents' centrality in the R&D process - patents' idiosyncrasies open peculiar challenges to the current NLP state of the art. This survey aims at a) describing patents' characteristics and the questions they raise to the current NLP systems, b) critically presenting previous work and its evolution, and c) drawing attention to directions of research in which further work is needed. To the best of our knowledge, this is the first survey of generative approaches in the patent domain.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 25 of its 94 references that are in the map. First posted on arXiv 30 Apr 2021.

Builds on

25 of 94 references.

References · 25

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
  4. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
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  6. 2020Big Bird: Transformers for Longer Sequencescited by 1,673
  7. 2017Get To The Point: Summarization with Pointer-Generator Networkscited by 1,281
  8. 2019PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarizationcited by 1,100
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13 more
  1. 2018Fast Abstractive Summarization with Reinforce-Selected Sentence Rewritingcited by 158
  2. 2019BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarizationcited by 155
  3. 2017Faithful to the Original: Fact Aware Neural Abstractive Summarizationcited by 141
  4. 2020CTRLsum: Towards Generic Controllable Text Summarizationcited by 98
  5. 2019On Extractive and Abstractive Neural Document Summarization with Transformer Language Modelscited by 83
  6. 2020Improving Zero and Few-Shot Abstractive Summarization with Intermediate Fine-tuning and Data Augmentationcited by 53
  7. 2019Patent Claim Generation by Fine-Tuning OpenAI GPT-2cited by 49
  8. 2020Fact-Enhanced Synthetic News Generationcited by 22
  9. 2017Patent Retrieval: A Literature Reviewcited by 17
  10. 2020CDEvalSumm: An Empirical Study of Cross-Dataset Evaluation for Neural Summarization Systemscited by 17
  11. 2020A Survey on Text Simplificationcited by 13
  12. 2020Neural Abstractive Summarization with Structural Attentioncited by 11
  13. 2020Prior Art Search and Reranking for Generated Patent Textcited by 8

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 2; 2025: 1; 2026: 1.

6 papers.

  1. 2024Natural Language Processing in the Patent Domain: A Surveycited by 20
  2. 2025PatentMind: A Multi-Aspect Reasoning Graph for Patent Similarity Evaluationcited by 5
  3. 2023Creating a silver standard for patent simplificationcited by 3
  4. 2024PatentEval: Understanding Errors in Patent Generationcited by 3
  5. 2023A novel approach to measuring the scope of patent claims based on probabilities obtained from (large) language modelscited by 0
  6. 2026Curation of a Cardiology Interface Terminology for Highlighting Electronic Health Records using Machine Learningcited by 0